textProjectionPlot: Plot words according to Supervised Dimension Projection.

In text: Analyses of Text using Transformers Models from HuggingFace, Natural Language Processing and Machine Learning

Plot words according to Supervised Dimension Projection.

Description

Plot words according to Supervised Dimension Projection.

Usage

textProjectionPlot(
  word_data,
  k_n_words_to_test = FALSE,
  min_freq_words_test = 1,
  min_freq_words_plot = 1,
  plot_n_words_square = 3,
  plot_n_words_p = 5,
  plot_n_word_extreme = 5,
  plot_n_word_frequency = 5,
  plot_n_words_middle = 5,
  titles_color = "#61605e",
  y_axes = FALSE,
  p_alpha = 0.05,
  overlapping = TRUE,
  p_adjust_method = "none",
  title_top = "Supervised Dimension Projection",
  x_axes_label = "Supervised Dimension Projection (SDP)",
  y_axes_label = "Supervised Dimension Projection (SDP)",
  scale_x_axes_lim = NULL,
  scale_y_axes_lim = NULL,
  word_font = NULL,
  bivariate_color_codes = c("#398CF9", "#60A1F7", "#5dc688", "#e07f6a", "#EAEAEA",
    "#40DD52", "#FF0000", "#EA7467", "#85DB8E"),
  word_size_range = c(3, 8),
  position_jitter_hight = 0,
  position_jitter_width = 0.03,
  point_size = 0.5,
  arrow_transparency = 0.1,
  points_without_words_size = 0.2,
  points_without_words_alpha = 0.2,
  legend_title = "SDP",
  legend_x_axes_label = "x",
  legend_y_axes_label = "y",
  legend_x_position = 0.02,
  legend_y_position = 0.02,
  legend_h_size = 0.2,
  legend_w_size = 0.2,
  legend_title_size = 7,
  legend_number_size = 2,
  group_embeddings1 = FALSE,
  group_embeddings2 = FALSE,
  projection_embedding = FALSE,
  aggregated_point_size = 0.8,
  aggregated_shape = 8,
  aggregated_color_G1 = "black",
  aggregated_color_G2 = "black",
  projection_color = "blue",
  seed = 1005,
  explore_words = NULL,
  explore_words_color = "#ad42f5",
  explore_words_point = "ALL_1",
  explore_words_aggregation = "mean",
  remove_words = NULL,
  n_contrast_group_color = NULL,
  n_contrast_group_remove = FALSE,
  space = NULL,
  scaling = FALSE
)

Arguments

word_data	Dataframe from textProjection
k_n_words_to_test	Select the k most frequent words to significance test (k = sqrt(100*N); N = number of participant responses). Default = TRUE.
min_freq_words_test	Select words to significance test that have occurred at least min_freq_words_test (default = 1).
min_freq_words_plot	Select words to plot that has occurred at least min_freq_words_plot times.
plot_n_words_square	Select number of significant words in each square of the figure to plot. The significant words, in each square is selected according to most frequent words.
plot_n_words_p	Number of significant words to plot on each(positive and negative) side of the x-axes and y-axes, (where duplicates are removed); selects first according to lowest p-value and then according to frequency. Hence, on a two dimensional plot it is possible that plot_n_words_p = 1 yield 4 words.
plot_n_word_extreme	Number of words that are extreme on Supervised Dimension Projection per dimension. (i.e., even if not significant; per dimensions, where duplicates are removed).
plot_n_word_frequency	Number of words based on being most frequent. (i.e., even if not significant).
plot_n_words_middle	Number of words plotted that are in the middle in Supervised Dimension Projection score (i.e., even if not significant; per dimensions, where duplicates are removed).
titles_color	Color for all the titles (default: "#61605e")
y_axes	If TRUE, also plotting on the y-axes (default is FALSE). Also plotting on y-axes produces a two dimension 2-dimensional plot, but the textProjection function has to have had a variable on the y-axes.
p_alpha	Alpha (default = .05).
overlapping	(boolean) Allow overlapping (TRUE) or disallow (FALSE) (default = TRUE).
p_adjust_method	Method to adjust/correct p-values for multiple comparisons (default = "holm"; see also "none", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr").
title_top	Title (default " ")
x_axes_label	Label on the x-axes.
y_axes_label	Label on the y-axes.
scale_x_axes_lim	Manually set the length of the x-axes (default = NULL, which uses ggplot2::scale_x_continuous(limits = scale_x_axes_lim); change e.g., by trying c(-5, 5)).
scale_y_axes_lim	Manually set the length of the y-axes (default = NULL; which uses ggplot2::scale_y_continuous(limits = scale_y_axes_lim); change e.g., by trying c(-5, 5)).
word_font	Font type (default: NULL).
bivariate_color_codes	The different colors of the words. Note that, at the moment, two squares should not have the exact same colour-code because the numbers within the squares of the legend will then be aggregated (and show the same, incorrect value). (default: c("#398CF9", "#60A1F7", "#5dc688", "#e07f6a", "#EAEAEA", "#40DD52", "#FF0000", "#EA7467", "#85DB8E")).
word_size_range	Vector with minimum and maximum font size (default: c(3, 8)).
position_jitter_hight	Jitter height (default: .0).
position_jitter_width	Jitter width (default: .03).
point_size	Size of the points indicating the words' position (default: 0.5).
arrow_transparency	Transparency of the lines between each word and point (default: 0.1).
points_without_words_size	Size of the points not linked with a words (default is to not show it, i.e., 0).
points_without_words_alpha	Transparency of the points not linked with a words (default is to not show it, i.e., 0).
legend_title	Title on the color legend (default: "(SDP)".
legend_x_axes_label	Label on the color legend (default: "(x)".
legend_y_axes_label	Label on the color legend (default: "(y)".
legend_x_position	Position on the x coordinates of the color legend (default: 0.02).
legend_y_position	Position on the y coordinates of the color legend (default: 0.05).
legend_h_size	Height of the color legend (default 0.15).
legend_w_size	Width of the color legend (default 0.15).
legend_title_size	Font size (default: 7).
legend_number_size	Font size of the values in the legend (default: 2).
group_embeddings1	Shows a point representing the aggregated word embedding for group 1 (default = FALSE).
group_embeddings2	Shows a point representing the aggregated word embedding for group 2 (default = FALSE).
projection_embedding	Shows a point representing the aggregated direction embedding (default = FALSE).
aggregated_point_size	Size of the points representing the group_embeddings1, group_embeddings2 and projection_embedding
aggregated_shape	Shape type of the points representing the group_embeddings1, group_embeddings2 and projection_embeddingd
aggregated_color_G1	Color
aggregated_color_G2	Color
projection_color	Color
seed	Set different seed.
explore_words	Explore where specific words are positioned in the embedding space. For example, c("happy content", "sad down").
explore_words_color	Specify the color(s) of the words being explored. For example c("#ad42f5", "green")
explore_words_point	Specify the names of the point for the aggregated word embeddings of all the explored words.
explore_words_aggregation	Specify how to aggregate the word embeddings of the explored words.
remove_words	manually remove words from the plot (which is done just before the words are plotted so that the remove_words are part of previous counts/analyses).
n_contrast_group_color	Set color to words that have higher frequency (N) on the other opposite side of its dot product projection (default = NULL).
n_contrast_group_remove	Remove words that have higher frequency (N) on the other opposite side of its dot product projection (default = FALSE).
space	Provide a semantic space if using static embeddings and wanting to explore words.
scaling	Scaling word embeddings before aggregation.

Value

A 1- or 2-dimensional word plot, as well as tibble with processed data used to plot.

See Also

See textProjection.

Examples

# The test-data included in the package is called: DP_projections_HILS_SWLS_100.
# The dataframe created by textProjection can also be used as input-data.

# Supervised Dimension Projection Plot
plot_projection <- textProjectionPlot(
  word_data = DP_projections_HILS_SWLS_100,
  k_n_words_to_test = FALSE,
  min_freq_words_test = 1,
  plot_n_words_square = 3,
  plot_n_words_p = 3,
  plot_n_word_extreme = 1,
  plot_n_word_frequency = 1,
  plot_n_words_middle = 1,
  y_axes = FALSE,
  p_alpha = 0.05,
  title_top = "Supervised Dimension Projection (SDP)",
  x_axes_label = "Low vs. High HILS score",
  y_axes_label = "Low vs. High SWLS score",
  p_adjust_method = "bonferroni",
  scale_y_axes_lim = NULL
)

plot_projection

# Investigate elements in DP_projections_HILS_SWLS_100.
names(DP_projections_HILS_SWLS_100)

text documentation built on Sept. 11, 2024, 7:22 p.m.